README.md

metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - twitter_pos_vcb
metrics:
  - accuracy
  - poseval
  - f1
  - recall
  - precision
model-index:
  - name: bert-base-cased-finetuned-Stromberg_NLP_Twitter-PoS_v2
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: twitter_pos_vcb
          type: twitter_pos_vcb
          config: twitter-pos-vcb
          split: train
          args: twitter-pos-vcb
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9853480683735223
language:
  - en
pipeline_tag: token-classification

bert-base-cased-finetuned-Stromberg_NLP_Twitter-PoS_v2

This model is a fine-tuned version of bert-base-cased on the twitter_pos_vcb dataset. It achieves the following results on the evaluation set:

• Loss: 0.0502
• $: {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 3}
• '': {'precision': 0.9312320916905444, 'recall': 0.9530791788856305, 'f1-score': 0.9420289855072465, 'support': 341}
• (: {'precision': 0.9791666666666666, 'recall': 0.9591836734693877, 'f1-score': 0.9690721649484536, 'support': 196}
• ): {'precision': 0.960167714884696, 'recall': 0.9703389830508474, 'f1-score': 0.9652265542676501, 'support': 472}
• ,: {'precision': 0.9988979501873485, 'recall': 0.9993384785005512, 'f1-score': 0.9991181657848325, 'support': 4535}
• .: {'precision': 0.9839189708141322, 'recall': 0.9894762249577601, 'f1-score': 0.9866897730281368, 'support': 20715}
• :: {'precision': 0.9926405887528997, 'recall': 0.9971072719967858, 'f1-score': 0.9948689168604183, 'support': 12445}
• Cc: {'precision': 0.9991067440821796, 'recall': 0.9986607142857142, 'f1-score': 0.9988836793927215, 'support': 4480}
• Cd: {'precision': 0.9903884661593912, 'recall': 0.9899919935948759, 'f1-score': 0.9901901901901902, 'support': 2498}
• Dt: {'precision': 0.9981148589510537, 'recall': 0.9976446837146703, 'f1-score': 0.9978797159492478, 'support': 14860}
• Ex: {'precision': 0.9142857142857143, 'recall': 0.9846153846153847, 'f1-score': 0.9481481481481482, 'support': 65}
• Fw: {'precision': 1.0, 'recall': 0.1, 'f1-score': 0.18181818181818182, 'support': 10}
• Ht: {'precision': 0.999877541023757, 'recall': 0.9997551120362435, 'f1-score': 0.9998163227820978, 'support': 8167}
• In: {'precision': 0.9960399353003514, 'recall': 0.9954846981437092, 'f1-score': 0.9957622393219583, 'support': 17939}
• Jj: {'precision': 0.9812470698546648, 'recall': 0.9834756049808129, 'f1-score': 0.9823600735322877, 'support': 12769}
• Jjr: {'precision': 0.9304511278195489, 'recall': 0.9686888454011742, 'f1-score': 0.9491850431447747, 'support': 511}
• Jjs: {'precision': 0.9578414839797639, 'recall': 0.9726027397260274, 'f1-score': 0.9651656754460493, 'support': 584}
• Md: {'precision': 0.9901398761751892, 'recall': 0.9908214777420835, 'f1-score': 0.990480559697213, 'support': 4358}
• Nn: {'precision': 0.9810285563194078, 'recall': 0.9819697621331922, 'f1-score': 0.9814989335846437, 'support': 30227}
• Nnp: {'precision': 0.9609722697706266, 'recall': 0.9467116357504216, 'f1-score': 0.9537886510363575, 'support': 8895}
• Nnps: {'precision': 1.0, 'recall': 0.037037037037037035, 'f1-score': 0.07142857142857142, 'support': 27}
• Nns: {'precision': 0.9697771061579146, 'recall': 0.9776564681985528, 'f1-score': 0.9737008471361739, 'support': 7877}
• Pos: {'precision': 0.9977272727272727, 'recall': 0.984304932735426, 'f1-score': 0.9909706546275394, 'support': 446}
• Prp: {'precision': 0.9983503349829983, 'recall': 0.9985184187487373, 'f1-score': 0.9984343697917544, 'support': 29698}
• Prp$: {'precision': 0.9974262182566919, 'recall': 0.9974262182566919, 'f1-score': 0.9974262182566919, 'support': 5828}
• Rb: {'precision': 0.9939770374552983, 'recall': 0.9929802569727358, 'f1-score': 0.9934783971906942, 'support': 15955}
• Rbr: {'precision': 0.9058823529411765, 'recall': 0.8191489361702128, 'f1-score': 0.8603351955307263, 'support': 94}
• Rbs: {'precision': 0.92, 'recall': 1.0, 'f1-score': 0.9583333333333334, 'support': 69}
• Rp: {'precision': 0.9802197802197802, 'recall': 0.9903774981495189, 'f1-score': 0.9852724594992636, 'support': 1351}
• Rt: {'precision': 0.9995065383666419, 'recall': 0.9996298581122763, 'f1-score': 0.9995681944358769, 'support': 8105}
• Sym: {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 9}
• To: {'precision': 0.9984649496844619, 'recall': 0.9989761092150171, 'f1-score': 0.9987204640450398, 'support': 5860}
• Uh: {'precision': 0.9614460148062687, 'recall': 0.9507510933637574, 'f1-score': 0.9560686457287633, 'support': 10518}
• Url: {'precision': 1.0, 'recall': 0.9997242900468707, 'f1-score': 0.9998621260168207, 'support': 3627}
• Usr: {'precision': 0.9999025388626285, 'recall': 1.0, 'f1-score': 0.9999512670565303, 'support': 20519}
• Vb: {'precision': 0.9619302598929085, 'recall': 0.9570556133056133, 'f1-score': 0.9594867452615125, 'support': 15392}
• Vbd: {'precision': 0.9592894152479645, 'recall': 0.9548719837907533, 'f1-score': 0.9570756023262255, 'support': 5429}
• Vbg: {'precision': 0.9848831077518018, 'recall': 0.984191111891797, 'f1-score': 0.9845369882270251, 'support': 5693}
• Vbn: {'precision': 0.9053408597481546, 'recall': 0.9164835164835164, 'f1-score': 0.910878112712975, 'support': 2275}
• Vbp: {'precision': 0.963605718209626, 'recall': 0.9666228317364894, 'f1-score': 0.9651119169688633, 'support': 15969}
• Vbz: {'precision': 0.9881780250347705, 'recall': 0.9861207494795281, 'f1-score': 0.9871483153872872, 'support': 5764}
• Wdt: {'precision': 0.8666666666666667, 'recall': 0.9285714285714286, 'f1-score': 0.896551724137931, 'support': 14}
• Wp: {'precision': 0.99125, 'recall': 0.993734335839599, 'f1-score': 0.9924906132665832, 'support': 1596}
• Wrb: {'precision': 0.9963488843813387, 'recall': 0.9979683055668428, 'f1-score': 0.9971579374746244, 'support': 2461}
• ``: {'precision': 0.9481865284974094, 'recall': 0.9786096256684492, 'f1-score': 0.963157894736842, 'support': 187}
• Accuracy: 0.9853
• Macro avg: {'precision': 0.9296417163691048, 'recall': 0.8931046018294694, 'f1-score': 0.8930917459781836, 'support': 308833}
• Weighted avg: {'precision': 0.985306457604231, 'recall': 0.9853480683735223, 'f1-score': 0.9852689858931941, 'support': 308833}

Model description

For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/Token%20Classification/Monolingual/StrombergNLP-Twitter_pos_vcb/NER%20Project%20Using%20StrombergNLP%20Twitter_pos_vcb%20Dataset%20with%20PosEval.ipynb.

Intended uses & limitations

This model is intended to demonstrate my ability to solve a complex problem using technology.

Training and evaluation data

Dataset Source: https://huggingface.co/datasets/strombergnlp/twitter_pos_vcb

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss	$	''	(	)	,	.	:	Cc	Cd	Dt	Ex	Fw	Ht	In	Jj	Jjr	Jjs	Md	Nn	Nnp	Nnps	Nns	Pos	Prp	Prp$	Rb	Rbr	Rbs	Rp	Rt	Sym	To	Uh	Url	Usr	Vb	Vbd	Vbg	Vbn	Vbp	Vbz	Wdt	Wp	Wrb	``	Accuracy	Macro avg	Weighted avg
0.0649	1.0	7477	0.0570	{'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 3}	{'precision': 0.9257142857142857, 'recall': 0.9501466275659824, 'f1-score': 0.9377713458755427, 'support': 341}	{'precision': 0.9690721649484536, 'recall': 0.9591836734693877, 'f1-score': 0.964102564102564, 'support': 196}	{'precision': 0.9427402862985685, 'recall': 0.9766949152542372, 'f1-score': 0.959417273673257, 'support': 472}	{'precision': 0.998677831643896, 'recall': 0.9993384785005512, 'f1-score': 0.9990080458503251, 'support': 4535}	{'precision': 0.977118886452474, 'recall': 0.9895244991552016, 'f1-score': 0.9832825654186554, 'support': 20715}	{'precision': 0.9933418899406385, 'recall': 0.9950180795500201, 'f1-score': 0.9941792782305006, 'support': 12445}	{'precision': 0.9995530726256984, 'recall': 0.9984375, 'f1-score': 0.9989949748743719, 'support': 4480}	{'precision': 0.9856573705179282, 'recall': 0.9903923138510808, 'f1-score': 0.9880191693290733, 'support': 2498}	{'precision': 0.9974431435876733, 'recall': 0.9975773889636609, 'f1-score': 0.9975102617589665, 'support': 14860}	{'precision': 0.9846153846153847, 'recall': 0.9846153846153847, 'f1-score': 0.9846153846153847, 'support': 65}	{'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 10}	{'precision': 0.9993879299791896, 'recall': 0.9996326680543651, 'f1-score': 0.9995102840352595, 'support': 8167}	{'precision': 0.996751246289139, 'recall': 0.9919727966999276, 'f1-score': 0.9943562807331248, 'support': 17939}	{'precision': 0.9833821318350875, 'recall': 0.9732163834286162, 'f1-score': 0.9782728489333229, 'support': 12769}	{'precision': 0.9160447761194029, 'recall': 0.9608610567514677, 'f1-score': 0.9379178605539636, 'support': 511}	{'precision': 0.9656357388316151, 'recall': 0.9623287671232876, 'f1-score': 0.9639794168096055, 'support': 584}	{'precision': 0.9894398530762167, 'recall': 0.9889857732905002, 'f1-score': 0.9892127610741337, 'support': 4358}	{'precision': 0.9773657120232282, 'recall': 0.9799847818175803, 'f1-score': 0.9786734946725035, 'support': 30227}	{'precision': 0.9524512029051294, 'recall': 0.9435637998875773, 'f1-score': 0.9479866719376517, 'support': 8895}	{'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 27}	{'precision': 0.9615001245950661, 'recall': 0.9796876983623207, 'f1-score': 0.9705087090486072, 'support': 7877}	{'precision': 1.0, 'recall': 0.984304932735426, 'f1-score': 0.9920903954802259, 'support': 446}	{'precision': 0.9984506567867969, 'recall': 0.9981816957370867, 'f1-score': 0.9983161581464269, 'support': 29698}	{'precision': 0.9967404357522731, 'recall': 0.9969114619080301, 'f1-score': 0.996825941494381, 'support': 5828}	{'precision': 0.9929754139488208, 'recall': 0.9922908179254152, 'f1-score': 0.9926329978996206, 'support': 15955}	{'precision': 0.9102564102564102, 'recall': 0.7553191489361702, 'f1-score': 0.8255813953488371, 'support': 94}	{'precision': 0.9324324324324325, 'recall': 1.0, 'f1-score': 0.965034965034965, 'support': 69}	{'precision': 0.9496810772501771, 'recall': 0.9918578830495929, 'f1-score': 0.9703113685734974, 'support': 1351}	{'precision': 0.9997531778353697, 'recall': 0.9995064774830351, 'f1-score': 0.9996298124383021, 'support': 8105}	{'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 9}	{'precision': 0.9976145851081956, 'recall': 0.9991467576791809, 'f1-score': 0.9983800835535852, 'support': 5860}	{'precision': 0.9536072530864198, 'recall': 0.9400076060087469, 'f1-score': 0.9467585942736763, 'support': 10518}	{'precision': 1.0, 'recall': 0.9997242900468707, 'f1-score': 0.9998621260168207, 'support': 3627}	{'precision': 0.9999025388626285, 'recall': 1.0, 'f1-score': 0.9999512670565303, 'support': 20519}	{'precision': 0.9477061276868785, 'recall': 0.9595893970893971, 'f1-score': 0.9536107434548213, 'support': 15392}	{'precision': 0.9504877599852751, 'recall': 0.9511880641002026, 'f1-score': 0.9508377830970356, 'support': 5429}	{'precision': 0.9805672268907563, 'recall': 0.9838398032671702, 'f1-score': 0.9822007891275757, 'support': 5693}	{'precision': 0.9150772025431426, 'recall': 0.8857142857142857, 'f1-score': 0.9001563547018092, 'support': 2275}	{'precision': 0.9659941631772617, 'recall': 0.953472352683324, 'f1-score': 0.9596924143581986, 'support': 15969}	{'precision': 0.9898548189609935, 'recall': 0.9817834836918806, 'f1-score': 0.9858026304328891, 'support': 5764}	{'precision': 0.8666666666666667, 'recall': 0.9285714285714286, 'f1-score': 0.896551724137931, 'support': 14}	{'precision': 0.9930904522613065, 'recall': 0.9906015037593985, 'f1-score': 0.9918444165621078, 'support': 1596}	{'precision': 0.9963459196102314, 'recall': 0.9971556277935798, 'f1-score': 0.9967506092607635, 'support': 2461}	{'precision': 0.9432989690721649, 'recall': 0.9786096256684492, 'f1-score': 0.9606299212598425, 'support': 187}	0.9829	{'precision': 0.8843643626705173, 'recall': 0.886420871781996, 'f1-score': 0.8851282596274812, 'support': 308833}	{'precision': 0.9827817256769859, 'recall': 0.9829292854066761, 'f1-score': 0.9828343183557866, 'support': 308833}
0.0396	2.0	14954	0.0502	{'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 3}	{'precision': 0.9312320916905444, 'recall': 0.9530791788856305, 'f1-score': 0.9420289855072465, 'support': 341}	{'precision': 0.9791666666666666, 'recall': 0.9591836734693877, 'f1-score': 0.9690721649484536, 'support': 196}	{'precision': 0.960167714884696, 'recall': 0.9703389830508474, 'f1-score': 0.9652265542676501, 'support': 472}	{'precision': 0.9988979501873485, 'recall': 0.9993384785005512, 'f1-score': 0.9991181657848325, 'support': 4535}	{'precision': 0.9839189708141322, 'recall': 0.9894762249577601, 'f1-score': 0.9866897730281368, 'support': 20715}	{'precision': 0.9926405887528997, 'recall': 0.9971072719967858, 'f1-score': 0.9948689168604183, 'support': 12445}	{'precision': 0.9991067440821796, 'recall': 0.9986607142857142, 'f1-score': 0.9988836793927215, 'support': 4480}	{'precision': 0.9903884661593912, 'recall': 0.9899919935948759, 'f1-score': 0.9901901901901902, 'support': 2498}	{'precision': 0.9981148589510537, 'recall': 0.9976446837146703, 'f1-score': 0.9978797159492478, 'support': 14860}	{'precision': 0.9142857142857143, 'recall': 0.9846153846153847, 'f1-score': 0.9481481481481482, 'support': 65}	{'precision': 1.0, 'recall': 0.1, 'f1-score': 0.18181818181818182, 'support': 10}	{'precision': 0.999877541023757, 'recall': 0.9997551120362435, 'f1-score': 0.9998163227820978, 'support': 8167}	{'precision': 0.9960399353003514, 'recall': 0.9954846981437092, 'f1-score': 0.9957622393219583, 'support': 17939}	{'precision': 0.9812470698546648, 'recall': 0.9834756049808129, 'f1-score': 0.9823600735322877, 'support': 12769}	{'precision': 0.9304511278195489, 'recall': 0.9686888454011742, 'f1-score': 0.9491850431447747, 'support': 511}	{'precision': 0.9578414839797639, 'recall': 0.9726027397260274, 'f1-score': 0.9651656754460493, 'support': 584}	{'precision': 0.9901398761751892, 'recall': 0.9908214777420835, 'f1-score': 0.990480559697213, 'support': 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'support': 94}	{'precision': 0.92, 'recall': 1.0, 'f1-score': 0.9583333333333334, 'support': 69}	{'precision': 0.9802197802197802, 'recall': 0.9903774981495189, 'f1-score': 0.9852724594992636, 'support': 1351}	{'precision': 0.9995065383666419, 'recall': 0.9996298581122763, 'f1-score': 0.9995681944358769, 'support': 8105}	{'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 9}	{'precision': 0.9984649496844619, 'recall': 0.9989761092150171, 'f1-score': 0.9987204640450398, 'support': 5860}	{'precision': 0.9614460148062687, 'recall': 0.9507510933637574, 'f1-score': 0.9560686457287633, 'support': 10518}	{'precision': 1.0, 'recall': 0.9997242900468707, 'f1-score': 0.9998621260168207, 'support': 3627}	{'precision': 0.9999025388626285, 'recall': 1.0, 'f1-score': 0.9999512670565303, 'support': 20519}	{'precision': 0.9619302598929085, 'recall': 0.9570556133056133, 'f1-score': 0.9594867452615125, 'support': 15392}	{'precision': 0.9592894152479645, 'recall': 0.9548719837907533, 'f1-score': 0.9570756023262255, 'support': 5429}	{'precision': 0.9848831077518018, 'recall': 0.984191111891797, 'f1-score': 0.9845369882270251, 'support': 5693}	{'precision': 0.9053408597481546, 'recall': 0.9164835164835164, 'f1-score': 0.910878112712975, 'support': 2275}	{'precision': 0.963605718209626, 'recall': 0.9666228317364894, 'f1-score': 0.9651119169688633, 'support': 15969}	{'precision': 0.9881780250347705, 'recall': 0.9861207494795281, 'f1-score': 0.9871483153872872, 'support': 5764}	{'precision': 0.8666666666666667, 'recall': 0.9285714285714286, 'f1-score': 0.896551724137931, 'support': 14}	{'precision': 0.99125, 'recall': 0.993734335839599, 'f1-score': 0.9924906132665832, 'support': 1596}	{'precision': 0.9963488843813387, 'recall': 0.9979683055668428, 'f1-score': 0.9971579374746244, 'support': 2461}	{'precision': 0.9481865284974094, 'recall': 0.9786096256684492, 'f1-score': 0.963157894736842, 'support': 187}	0.9853	{'precision': 0.9296417163691048, 'recall': 0.8931046018294694, 'f1-score': 0.8930917459781836, 'support': 308833}	{'precision': 0.985306457604231, 'recall': 0.9853480683735223, 'f1-score': 0.9852689858931941, 'support': 308833}

Framework versions

• Transformers 4.28.1
• Pytorch 2.0.0
• Datasets 2.11.0
• Tokenizers 0.13.3